CloudForest
go-fann
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| CloudForest | go-fann | |
|---|---|---|
| 4 | - | |
| 735 | 115 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | about 9 years ago | |
| Go | Go | |
| GNU General Public License v3.0 or later | MIT License |
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CloudForest
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Trinary Decision Trees for missing value handling
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~10 years ago and it worked well.
It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.
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Future of Golang
Personally, my Go-to ML tool for tabular data is here: https://github.com/ryanbressler/CloudForest
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[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
go-fann
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Tracking mentions began in Dec 2020.
What are some alternatives?
libsvm - libsvm go version
GoLearn - Machine Learning for Go
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
go-galib - Genetic Algorithms library written in Go / golang
gobrain - Neural Networks written in go
gosseract - Go package for OCR (Optical Character Recognition), by using Tesseract C++ library
sklearn - bits of sklearn ported to Go #golang
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
bayesian - Naive Bayesian Classification for Golang.
goga - Golang Genetic Algorithm
goml - On-line Machine Learning in Go (and so much more)